Conformance checking is a sub-discipline of process mining, which compares observed process traces with a process model to analyze whether the process execution conforms with or deviates from the process design. Organizations can leverage this analysis, for example to check whether their processes comply with internal or external regulations or to identify potential improvements. Gaining these insights requires suitable visualizations, which make complex results accessible and actionable. So far, however, the development of conformance checking visualizations has largely been left to tool vendors. As a result, current tools offer a wide variety of visual representations for conformance checking, but the analytical purposes they serve often remain unclear. However, without a systematic understanding of these purposes, it is difficult to evaluate the visualizations’ usefulness. Such an evaluation hence requires a deeper understanding of conformance checking as an analysis domain. To this end, we propose a task taxonomy, which categorizes the tasks that can occur when conducting conformance checking analyses. This taxonomy supports researchers in determining the purpose of visualizations, specifying relevant conformance checking tasks in terms of their goal, means, constraint type, data characteristics, data target, and data cardinality. Combining concepts from process mining and visual analytics, we address researchers from both disciplines to enable and support closer collaborations.

Modern business processes are subject to an increasing number of external and internal regulations. Compliance with these regulations is crucial for the success of organizations. To ensure this compliance, process managers can identify and mitigate deviations between the predefined process behavior and the executed process instances by means of conformance checking techniques. However, these techniques are inherently reactive, meaning that they can only detect deviations after they have occurred. It would be desirable to detect and mitigate deviations before they occur, enabling managers to proactively ensure compliance of running process instances. In this paper, we propose Business Process Deviation Prediction (BPDP), a novel predictive approach that relies on a supervised machine learning model to predict which deviations can be expected in the future of running process instances. BPDP is able to predict individual deviations as well as deviation patterns. Further, it provides the user with a list of potential reasons for predicted deviations. Our evaluation shows that BPDP outperforms existing methods for deviation prediction. Following the idea of action-oriented process mining, BPDP thus enables process managers to prevent deviations in early stages of running process instances. • A new approach to predict individual deviations and deviation patterns. • Addresses challenge of label imbalance by undersampling the training data. • Addresses challenge of action orientation with weighted loss function. • Experimentally derives the best supervised machine learning strategy. • Demonstrates applicability by providing managers with information on non-conformity.

Process mining aims to derive insights into business processes from event logs recorded from information systems. Process discovery algorithms construct process models that describe the executed process. With the increasing availability of large-scale event logs, process discovery has shifted towards a data-oriented research discipline, aiming to design algorithms that are applicable and useful in practice. This shift has revealed a fundamental problem in process discovery research: Currently, contributions can only be considered in isolation. Researchers conduct experiments to show that they move the field forward, but due to a lack of reliability and validity, the individual contributions are hard to generalize. In this article, we argue that one reason for these problems is the lack of conventions or standards for experimental design in process discovery. Hence, we propose “process discovery engineering”: a research methodology for process discovery, consisting of a shared terminology and a checklist for conducting experiments. We demonstrate its applicability by means of an example experimental evaluation of process discovery algorithms and discuss the implications of the methodology on the field. This article is not meant to be prescriptive but to invite and encourage the community to contribute to this discussion to advance the field as a whole.

User interaction (UI) logs are high-resolution event logs that record low-level activities performed by a user during the execution of a task in an information system. Each event in such a log represents an interaction between the user and the interface, such as clicking a button, ticking a checkbox, or typing into a text field. UI logs are used in many different application contexts for purposes such as usability analysis, task mining, or robotic process automation (RPA). However, UI logs suffer from a lack of standardization. Each research study and processing tool relies on a different conceptualization and implementation of the elements and attributes of user interactions. This exacerbates or even prohibits the integration of UI logs from different sources or the combination of UI data collection tools with downstream analytics or automation solutions. In this paper, our objective is to address this issue and facilitate the exchange and analysis of UI logs in research and practice. Therefore, we first review process-related UI logs in scientific publications and industry tools to determine commonalities and differences between them. Based on our findings, we propose a universally applicable reference data model for process-related UI logs, which includes all core attributes but remains flexible regarding the scope, level of abstraction, and case notion. Finally, we provide exemplary implementations of the reference model in XES and OCED.

The compliance of business processes is crucial for the success of organizations. To ensure it, process managers identify and mitigate deviations between the predefined process behavior and the executed process instances. Approaches that can predict such deviations in running process instances before they occur enable companies to proactively enforce process compliance. However, existing techniques cannot predict the exact deviation type or cope with the imbalanced nature of this prediction task. In this paper, we propose Business Process Deviation Prediction (BPDP), a novel predictive approach that relies on a supervised machine learning model to predict which deviations can be expected in the future of running process instances. Our evaluation shows that BPDP outperforms existing methods in predicting which deviation will occur. Further, we identify process characteristics that influence the likelihood for a deviation. Following the idea of action-oriented process mining, BPDP thus enables process managers to prevent deviations in early stages of running process instances.

The goal of process discovery is to visualize event log data as a process model. In reality, however, these models are often highly complex. Process trace clustering is a well-studied and powerful technique to address this. It groups an event log into more cohesive sub logs, such that the discovered process models become less complex and easier to understand. Over the past 15 years, researchers proposed various approaches for trace clustering in process discovery. The developed approaches vary greatly with regard to algorithmic capacities, data characteristics, computational complexity, and integration of additional information. In this paper, we provide a state-of-the-art analysis of trace clustering by a) performing a systematic literature review, and b) proposing a generic framework for trace clustering. Eventually, our goal is to provide an overview of current trace clustering research and a basis for developing new methods and approaches to trace clustering.

Cloud computing offers readily available, scalable infrastructure to tackle problems involving high data volume and velocity. Discovering processes from event streams, especially when the business processes execute in a cloud environment, is such a problem. Event stream data is generated rapidly with varying volume and must be processed on-the-fly, making stream processing an important use case for cloud computing. This paper describes a distributed, streaming implementation of the flexible heuristics miner on Amazon Kinesis, a cloud-based event stream infrastructure, showing how mining methods can scale effortlessly to tens of millions of events per minute.